1. Introduction
In this paper are described methods for determining intensities of satisfactions and desires (tensions of needs) of virtual creature vP (e g. a motivated agent system, a virtual human in Internet) The state (the intensity) of satisfaction and desire of virtual creature vP, with respect to need b (at time t), is represented by functions bef(vP,b,t) und des(vP,b,t). These functions were introduced (not formal) and used in Schurmann [AS4] (1998), [AS3] (1998), [AS1] (2000). The description of the patent application [AS1] (2000) is based on function values bef(vP,b,t) and des(vP,b,t). Until now, there is no method for determining intensities of satisfaction bef(vP,b,t) and desire des(vP,b,t) for an artificial creature vP. The methods for determining intensities bef(vP,b,t) and des(vP,b, t) described in this paper can be applied in order.
i. to simulate satisfactions and desires of an artificial creature vP (e g a motivated agent system, a virtual human or mammal in Internet or entertainment software) On the basis of patent description [AS1] (2000), emotion states of vP may with that be formal represented;
ii. to develop motivation function and control algorithm (e.g. as described in Schurnann [AS4] (1998), Sect. 2.7), for artificial creature vP, which determine the behaviour of vP;
iii. to build in such semantic of emotions into creature vP that vP would better understand human emotions.
The methods presented here use notions, functions and methods described in Schurmann [AS1] (2000). These notions and functions are presented in short in Sect. 2 of this paper. In Sect. 3 is given a method for determining intensities of satisfaction, bef(vP,b,t), and desire, des(vP,b,t), by stimulus patterns in situation models and activity descriptions, for needs which are individually associated with situations or activities: GE (to be healthy), AN (for recognition, acknowledgment and self-esteem), LE (to be alive), KS (to have no pain), SE (for sexual relations), NU (to be in normal environment), BW (for bodily activities), SN (for tasty food), SH (for visual beauty). The greatest part of this paper (Sect. 4) contains descriptions of methods for determining intensities bef(vP,b,t) and des(vP,b,t) for standard situations of vP, for the following needs: AU (for attention and identification), NE (curiosity and the need for knowledge), GR (to belong to communities), MA (to have power over people and animals), LI (for liking and love), MB (material and financial needs), BZ(Sz) (to achieve goal situation Sz), NA (to have children), BH(OK) (to help object OK).
2. Notations and Functions used in my Patent Description [AS1]
We use, in this paper, following notions and functions described in my paper [AS I] (2000), Artificial creature vP has a set Bd(vP) of needs. To Bd(vP) belong e.g. GR (belong to communities), MA (have power), MB (material and financial needs), BZ(Sz) (achieve goal situation Sz).
The state of tension (desire) and satisfaction of vP, with respect to need b, at time 1, is given by two functions:
0xe2x89xa6des(vP,b,t)xe2x89xa660, xe2x88x9230xe2x89xa6bef(fP,b,t)xe2x89xa630, for bxcex5Bd(vP) 
where des(vP,b,t) is the intensity of desire and bef(vP,b,t) the intensity of satisfaction (or dissatisfaction) of vP, with respect to need b, at time t. These functions have the following properties.
i. Increasing function bef(vP,b,t) means vP satisfies his/her need b (positive stimulus) and is perceived by vP with approval, joy or happiness.
ii. When bef(vP,b,t) less than 0 and does not increase then vP perceives bef(vP,b,t) as a negative stimulus (disappointment, annoyance, sadness, suffering) with respect to need b. Decreasing bef(vP,b,t) less than 0 means stronger negative stimulus with regard to need b.
iii. des(vP,b,t) is the intensity of desire of vP to satisfy need b at time L. The greater des(vP,b,t) the greater is the desire of vP to satisfy need b. des(vP,b,t) less than 0.5 means xe2x80x98need b of vP is well satisfied at time txe2x80x99.
iv. The greater des(vP,b,t) the greater is the approval and joy of vP when bef(vP,b,t) increases, and the greater is the dissatisfaction, annoyance and grief of vP when bef(vP,b,t) less than 0 and decreases.
Artificial creature vP has models of objects and situations (OSM) of his/her/its surrounding, and models (schemes) of activities (behaviours, operations, procedures) which vP may execute. Object, situation or activity, OSA, has the same structure as its object, situation or activity model, respectively. Stimulus patterns. In model OSM (and OSA) stimuli are represented by patterns (called stimulus patterns) of the following form:
(2.1) ([∘|(Nba, Nb),]fs(vP,b)=([∘|p;]n;(y1,z1), . . . ,(yn,zn); q ht) [∘|/ z eu][∘|;OSM1.Ej] [∘|; where C]) 
where [tex1|. . . |texk] denotes one of the words tex1, . . . , texk, ∘ is the empty word, Nba, Nb and n are natural numbers, NBaxe2x89xa6Nb, 1xe2x89xa6nxe2x89xa610),fs denotes name of a stimulus pattern, 0xe2x89xa6pxe2x89xa61, xe2x88x9230xe2x89xa6yixe2x89xa630, xe2x88x9255xe2x89xa6zixe2x89xa660, yi and zi are simple arithmetical expressions, q hi denotes a time period (e g: 0.5 h, 3 days, 1 week), n*q htxe2x89xa6720 h, z greater than 0, eu denotes a measure (e g kg, g, h, km, m, l) and e g/200 g denotes xe2x80x98pro 200 gxe2x80x99. Nba/Nb is the probability that the pattern fs(vP,b)=( . . . ) is valid. C is a condition. If C occurs then [∘|(Nba,Nb),]fs(vP,b)=( . . . ) can be applied only if C is true. If OSM1.Ej occurs then the pattern fs(vP,b)=( . . . ) concerns the pattern Ej=(xe2x80x98dsxe2x80x99,( . . . fse(vP,b)=. . . )) in OSM1. Example of a pattern (fs=epb) occurring in OSM:
(2.2) epb(vP,b)=(n;(y1,z1), . . . ,(yn,zn);q ht) [∘|/ z eu] 
where yn greater than 1+y1 and z1 greater than 1+zn. The meaning: vP can execute (time t) an activity, AV, such that when vP uses OSM in AV then vP expects that OSM will increase bef(vP,b,.) and decrease des(vP,b,.) according to the pattern (2.2). Exact description of all patterns and their meanings is given in Schurmann [AS1], Sect. 2.2.
Intensity of stimulus. Expected (by vP, at time t) intensity of positive stimulus of pattern (2.1) is given by epr(vP,OSM,fsp,b, . . . ,t) (defined in [AS1], Sect. 2.3.1), where fsp denotes the following (positive) pattern names: epb, upb, vnb, epbu, upbu. Let fsn denotes the following names of (negative) patterns: enb, unb, enbu, unbu, vpb, vnb. Expected (by vP, at time t) intensity of negative stimulus of the pattern (2.1) (where fs=fsn) is given by enr(vP,OSM,fsn,b, . . . ,t) (defined in [AS1], Sect. 2.3.2). The intensity of positive stimulus of OSM (time t) is given by
pros(vP,OSM,t)=xcexa3bxcex5Bp epr(vP,OSM,fsp,b, . . . ,t) 
where Bp={bxcex5 WB|( . . . fsp(vP,b)=.) is in OSM}, WB={bxcex5 Bd(vP)|des(vP,b,t) greater than 0.33* mdes(vP,t) } and mdes(vP,t)=max(des(vP,b,t), for b xcex5 Bd(vP)). The intensity of negative stimulus of OSM (time t) is given by:
nros(vP,OSM,t)=xcexa3bxcex5Bnenr(vP,OSM,fsn,b, . . . ,t) 
where Bn={bxcex5WB|( . . . fsn(vP,b)=. . . ) is in OSM}A. The intensity of stimulus of OSM at time t (s. [AS1], Sect. 2.3.3):
rosa(vP,OSM,t)=pros(vP,OSM,t)xe2x88x92nros(vP,OSM,t). 
Intensities of feelings. The states of feelings of vP: contentment, joy, happiness, dissatisfaction, annoyance and suffering, with respect to need b, at time t, are represented by function values zful(vP,b,t) (how they are determined is described in [AS1], Sect. 3). zful(vP,b,t) is interpreted as follows:
0xe2x89xa6zful(vP,b,t)xe2x80x94the intensity of contentment (the small values), joy (the middle values), happiness (the great values) of vP, with regard to need b;
0 greater than zful(vP,b,t)xe2x80x94the intensity of dissatisfaction (the greater values), annoyance, grief, sadness and suffering (the smaller values) of vP with regard to need b.
Intensities of liking, affection, love, dislike, annoyance and anger to/for an object, a situation or an activity (OS4) are given by two functions (how they are determined is described in [AS1] Sect. 4):
zulieb(vP,OSA,t)xe2x80x94the intensity of liking, affection and love of vP to/for OSA at time txe2x80x94the greater this value the stronger is the positive feeling of vP to OSA;
abhas(vP,OSA,t)xe2x80x94the intensity of dislike, aversion and anger of vP to/for OSA at time txe2x80x94the greater this value the stronger is the negative feeling of vP to OSA.
3. Determination of bef(vP,b,t) and des(vP,b,t) by Stimulus Patterns
Artificial creature vP has a set WPI(vP) of perceiving procedures which identify objects, situations and activities in the surrounding of vP. WPI(vP) contains procedures for: visual identification of objects and situations, identification of artificial creatures and real persons by names and passwords, perceiving objects by touch, syntactic and semantic identification of clauses. vP sends intensities of desires, satisfactions and emotions, to other artificial creatures, as values des(vP,b,t), bef(vP,b,t), zful(vP,b,t), zulieb(vP,OS4,t), abhas(vP,OSA,t), . . . . To people, vP expresses these values by clauses.
In this section we consider the determination of values bef(vP,b,t) and des(vP b,t) by stimulus patterns occurring in situation and activity models. For needs b in {GE (be healthy), LE (be alive), KS (have no pain), NU (be in normal environment), SH (visual beauty)}, these values are determined only by stimulus patterns. When vP perceives an object, Ob, then Ob is a component of a situation, S(Ob), which vP has perceived. The alteration of bef(vp,b,t) and des(vP,b,t), caused by object Ob in situation S(Ob), is determined by S(Ob). Therefore, we consider (in this section) only changes of bef(vP,b,t) and des(vP,b,t) caused by situations and activities. Object and situation models, OS, may contain several stimulus patterns ( . . . fsj(vP,b)=. . . where Cj), for j=1, . . . j1. Cj may have the form in AF1{circumflex over ( )}. . . This means: this pattern is valid only when OS is in activity AF1.
When vP has identified, by procedures in WPI(vP), that he/she/it is in new situation, SMn, which contains stimulus pattern . . . efs(vP,b)= . . . , then values bef(vP,b,t) and des(vP,b,t) are determined by this pattern (as shown below) if its priority is actually high enough, where efs differs from upb, unb, upbu, unbu. For this purpose, we associate to each stimulus pattern, in situation or activity model, a priority number as follows: (pr=xe2x80x2r, (Nba, Nb), s(vP,b)=. . . ), where r equals 1 or 2 and determines the priority of this stimulus pattern.
3.1. Determination of bef(vP,b,t) and des(vP,b,t) when vP has perceived a situation
Case: In situation model SMn is, for need b, only one pattern of the form
(3.1) (pr=xe2x80x2r, (Nba, Nb), efs(vP,b)=(n;(y1,z1), . . . , (yn, zn); q ht) [∘|/ z eu [∘|; where C]) valid (i e condition C is true) at time t, where efs denotes epb, epbu, enb, enbu. If r=1 then pattern (3.1) is applied, with probability Nba/Nb, to calculation of values bef(vP,b,t+i*q ht) and des(vP,b,t+i*q ht) (i=1, . . . ilxe2x89xa7n) by the method given in Schurmann [AS1] (2000), Sect. 2.2. xe2x80x98Pattern (3.1) is applied with probability pxe2x80x99 means: (3.1) is applied if los(1,a)=1 (if los(1,a)=0 then (3.1) is not be applied), where a[1]=p and los is defined as follows:
function los(k: integer, var ap: array[1 . . . 40] of real): integer;
var i: inleger; const am: array[1 . . . 40] of char=(xe2x80x98axe2x80x99. . . xe2x80x98zxe2x80x99,xe2x80x980xe2x80x99. . . xe2x80x989xe2x80x99, xe2x80x98+xe2x80x99, xe2x80x98xe2x88x92xe2x80x99, xe2x80x98/xe2x80x99, xe2x80x98:xe2x80x99);
begin Ur:=box with 10000 not marked balls;
for i:=I to k do begin mark ap[i]*10000 not marked balls with the sign am[i] end;
choose randomly a ball from Ur;
if the chosen ball is not marked then los:=0 else
begin i:=0; repeat i:=i+1 until (the chosen ball is marked with the sign am[i]);
los:=i end end los.
If in (3.1) r=2 (the priority) and bef(vP,b,t+q ht) and des(vP,bt+q ht) are not determined or determined by a stimulus pattern with priority 2 then pattern (3.1) is applied, with probability Nba/Nb, to calculation of values bef(vP,b,t+e*q ht) and des(vP,b,t+e*q ht) (e=1, . . . ,e1xe2x89xa7n) as in the case where r=1. If in (3.1) r=2 and bef(vP,b,t+q ht) and des(vP,b,t+q ht) are determined by a pattern with priority 1 then pattern (3.1) is not applied at time t.
Case: In situation SMn, for need b (and the pattern in SV.EvI), occurs only one pattern of the form (xe2x80x98pr=xe2x80x99r, (Nba, Nb), vsb(vP,b)=(p; n;(y11,z11), . . . , (y1n,z1n); q ht) [∘|/ z eu]; SV.Ev1 [∘|; where C])
where s denotes the letter xe2x80x98nxe2x80x99 or xe2x80x98pxe2x80x99, SV is a situation or activity model, C is true and (xe2x80x98pr=xe2x80x99r, (N1a, N1), esb(vP,b)=(n;(y1,z1), . . . , (yn,zn); q ht) [∘|/z eu] ∘|; where C1]) occurs in SV.Ev1. If bef(vP,b,t+k*q ht) and des(vP,b,t+k*q ht) are determined by the pattern in SV.Ev1, for k=1, . . . ,m, then execute the following operations, with probability Nba/Arb (i.e. if los(1,a)=1 and a[1]=Nba/Nb):
bef(vP,b,t+j*q ht)=bef(vP,b,t+j*q ht)+p*(y11xe2x88x92y1);
des(vP,b,t+j*q ht):=des(vP,b,t+j*q ht)+p*(z11xe2x88x92z1) for j=1, . . . ,mxe2x88x92n;
bef(vP,b,t+(mxe2x88x92n+i)*q ht):=bef(vP,b,t+(mxe2x88x92n+i)*q ht)+p*(y1ixe2x88x92yi);
des(vP,b,t+(mxe2x88x92n+i)*q ht):=des(vP,b,i+(mxe2x88x92n+i)*q ht)+p*(z1ixe2x88x92zi) for ixe2x88x921, . . . ,n.
Case Following m patterns occur in SMn for need b
(3.2) (pr=xe2x80x2r, (Nbaj, Nb), efsj(vP,b)=(nj;(yj2, zj1), . . . ,(yjnj, zjnj); qj ht) [∘|/zj euj] [∘|; where Cj]), fur j=1, . . . ,m, where efsj denotes epb, epbu, enb, enbu, Nba1+. . . +Nbamxe2x89xa6Nb and condition Cj holds at time t. If r=1 or r=2 and bef(vP,b,t+xt) und des(vP,b,t+xt) (xt greater than 0) are not determined by pattern with priority 1 then execute the following operations:
a[j]:=Nbaj/Nb, for=1, . . . ,m;
e:=los(m,a) {pattern e has been chosen if e greater than 0};
if e=0 then ignore all patterns (3.2) (no pattern is applied);
if e greater than 0 then calculate values bef(vP,b,t+k*qe hte) and des(vP,b,t+k*qe hte) by the pattern efse(vP,b)=(ne;(ye1,ze1), . . . (in the same way as by pattern (3.1))
3.2. Determination of Values bef(vP,b,t) and des(vP,b,t) when an Activity is Executed
bef(vP,b,t) and des(vP,b,t) change when vP executes activity or an activity uses vP (as an object). Model of an activity (or activity scheme, in short: activity) has the following form
AV begin {Ea1; . . . ;Eam}; (SG,KA) end 
where Eai denotes a property (e g stimulus pattern) of the activity AV and (SG,KA) is a directed graph, without isolated nodes, where nodes are situation models. To each edge (SMi, . . . ,SMj) (in KA) is associated sub-activity
sbeh(SMi,SMj)=((Nja, Nj), fADij, Dsij) 
where fADij is a sequence of elementary activities and operations which lead, with probability Nja/Nj, from situation SMi to situation SMj (when this sequence is executed) and Dsij is a set of stimulus patterns. Activities (behaviour schemes) are described more exactly in Schurmann [AS3] (1998), Sect. 4.1.
When activity AV is executed (by vP) then bef(vP,b,t) and des(vP,b,t) are determined as follows When vP is in situation SMi and (SMi,SMje) (for exe2x89xa6e1) are in KA then vP executes a sub-activity sbeh(SMi,SMjn)=((Njna, Njn), fADijn, Dsijn) (i e a sequence of elementary activities belonging to fADijn) and achieves in this way next situation SMjn. bef(vP,b,t+xt) und des(vP,b,t+xt) are determined, at first, by stimulus patterns in Dsijn and after that by patterns occurring in SMjn, as described in Sect. 3.1
4. Intensities bef(vP,b,t) und des(vP,b,t) in Standard Situations
In this section are described methods for determining bef(vP,b,t+xt) and des(vP,b,t+xt) for needs AU, NE, GR, MA, LI, MB, NA, BZ(Sz) and BH(OK).
bef(vP,AU,t); AUxe2x80x94need for attention and identification. bef(vP,AU,t) changes in following cases (a1) caused by time and depending on state of relaxation of vP, (a2) when perceiving objects and situations, (a3) when executing activities. In (a1) is determined the ground attention AU of vP after a sleep. The greatest attention is perceived neither as pain nor as grief
xe2x88x924xe2x89xa6cauxe2x89xa6bef(vP,AU,t)xe2x89xa6oauxe2x89xa625, cau less than 2, 5xe2x89xa6oau. 
Attention (a2) and (a3) is directed to objects, situations and elementary activities/operations When des(vP,AU,t) increases (decreases) then the attention of vP increases (decreases, respectively). The behaviour xe2x80x98sleepxe2x80x99 increases
bef(vP,AU,t) up to oauxe2x80x940.5. We distinguish the following kinds of attention.
AUw(UOS)xe2x80x94attention when vP is identifying surrounding, object or situation UOS,
AUa(AVe)xe2x80x94attention when vP is executing (elementary) activity/operation AVe.
The following rules AU1, . . . ,AU3 have priority 1.
AU1 Let ts denotes the time 5 min after good sleep of vP. The ground attention of vP is determined as follows
bef(vP,AU,ts)=oau xe2x88x922, des(vP,AU,ts)=5.
Every r min (after time ts) are performed the following operations
dau1 bef(vP,AU,ts+(ixe2x88x921)*r min)+gda*sqrt(i*2)*(oau+5xe2x88x92bef(vP,AU,ts+(ixe2x88x921)*r min));
dau2.=xe2x88x92ga1*sqrt(8+des(vP,ES,ts+1*r min); dau:=dau1+dau2;
bef(vP,AU,ts+i*r min):=max(min(dau, oauxe2x88x921.5), cau);
des(vP,AU,ts+i*r min).=2.1*(oauxe2x88x92bef(vP,AU,ts+1*rmin); for i=1, . . . ,i1xe2x89xa6865
(3 days=24 h*3=72*60 min, 72*60/5=864), where 1xe2x89xa6rxe2x89xa620, ES denotes the need for relaxation, 0.001xe2x89xa6gdaxe2x89xa60.1, 0.1xe2x89xa6ga1 less than 1 (sugg.: r=5, gda=0.011, gal=0.4, oau=20, cau=xe2x88x923)
AU2. vP has noticed (at time t) a new object or situation, OS, in part TUs of surrounding U(vP,t). The intensity of attention AUw directed to TUv equals:
bef(vP,AUw(TUs),t):=bef(vP,AU,t)xe2x88x923; des(vP,AUw(TUs),t):=2.1*(oauxe2x88x92bef(vP,AUw(TUs),t)). 
OS may be also a sequence of words which denotes an object, a situation or an activity
AU2.1. When vP identified (time t1) object or situation OS in TUs (thus vP has sufficient exact model of OS) then:
bef(vP,AUw(TUs),t1).=bef(vP,AU,t1); des(vP,AUw(TUs),t):=des(vP,AU,t1);
aw1.=gau*sqrt((des(vP,UA,t1)+0.2)*(0.333+max(pros(vP,OS,t1), nros(vP,OS,t1))));
if aw1xe2x89xa6oauxe2x88x92cau then bef(vP,AUw(OS),t1).=oauxe2x88x922xe2x88x92aw1 else bef(vP,AUw(OS),t1).=cauxe2x88x922;
des(vP,AUw(OS),t1).=2.1*(oauxe2x88x92bef(vP,AUw(OS),t1));
where AUw(OS) is the attention directed to OS, 0.01xe2x89xa6gauxe2x89xa60.1 (sugg. gau=0.0317). After vP finished observation of OS (time t2) in his/her/its surrounding U(vP,t2) then:
bef(vP,AUw(OS),t2):=oauxe2x88x920.5; des(vP,AUw(OS),t2):=1. 
AU2.2 When vP is not able to identify OS (i e vP cannot associate with OS an appropriate model) then.
bef(vP,AUw(OS),t1)=bef(vP,AU,t1)xe2x88x922.5; des(vP,AUw(OS),t1):=2.1*(oauxe2x88x92bef(vP,AUw(OS),t1)). 
When vP has build model, OSM, of OS then determine values bef(vP,AUw(OS),t1) and des(vP,AUw(OS),t) as given in AU2 1. If vP has not build model of OS (time t3) then:
bef(vP,AUw(TUs),t3):=bef(vP,AU,t3); des(vP,AUw(TUs),t3):=1.5*(oauxe2x88x92bef(vP,AU,t3)); 
bef(vP,AUw(OS),t3):=max(bef(vP,AU,t3), 1); des(vP,AUw(OS),t3).=1.5*(oauxe2x88x92bef(vP,AU,t3)). 
AU3 vP executes activity, AVa, different from a passive activity like xe2x80x98sleepxe2x80x99 or xe2x80x98lie relaxedxe2x80x99 The attention of vP is directed to sub-activity sbeh(SMi,SMj)=(.fADij,Dsij) which vP is executing, where fADy is the sequence of elementary activities which vP is executing and Dsij is the set of stimulus patterns connected with activities in fADij. The following operations are performed before execution offADij (where gau is given in AU2.1):
aw1.=gau*sqrt((des(vP,UA,t)+0.2)*(0.333+max(pros(vP,Dsij,t), nros(vP,Dsij,t))));
if aw1xe2x89xa6oauxe2x88x92cau then bef(vP,AUa(fADij),t):=oauxe2x88x922xe2x88x92aw1 else bef(vP,AUa(fADij),t):=cauxe2x88x922;
des(vP,AUa(fADij),t).=2.1*(oauxe2x88x92bef(vP,AUa(fADij),t));
aw2.=gau*sqrt((des(vP,UA,t)+0.2)*(0.333+max(pros(vP,SMj,t), nros(vP,SMj,t))));
if aw2xe2x89xa6oauxe2x88x92cau then bef(vP,AUw(SMj),t)=oauxe2x88x922xe2x88x92aw2 else bef(vP,AUw(SMj),t).=cauxe2x88x922;
des(vP,AUw(SMj),t)=2.1*(oauxe2x88x92bef(vP,AUw(SMj),t)).
After vP has executed activities fADO (time t1) then
bef(vP,AUa(fADij),t1).=oauxe2x88x921.5; des(vP,AUa(fADij),t1):=3. 
After vP finished observation of SMj (time t2) then.
bef(vP,AUw(SMj),t2):=oauxe2x88x920.5; des(vP,AUw(SMj),t2):=1. 
bef(vP,NE,t); NVExe2x80x94curiosity and need for knowledge. There are 3 kinds of the need NE:
NEw(OS)xe2x80x94when vP perceives object or situation OS,
NEk(OSM)xe2x80x94need for knowledge of object or situation model OSM and models associated with OSM,
NEz(SM)xe2x80x94how can situation SM be achieved.
We assume that vP has a set (KAO) of cognition algorithms and operations. Examples: for building visual representation of object or situation, algorithms for perceiving properties of objects and situations (e g touch properties of an object, motion properties), algorithms for naming objects, situations and activities (situation can be named by a clausexe2x80x94as described in Schurmann [AS2] (1999)), algorithms for verifying and (eventual) correcting consistency and completeness of object and situation models, algorithms for reasoning about surrounding of vP (some such algorithms are given in Schurmann [AS3] (1998)).
Below, x denotes letters w, k or z in the contexts NEx, onx, cnx, rnx. Let 3xe2x89xa6onexe2x89xa620 and xe2x88x9212xe2x89xa6cne greater than 0, where one, cne depend on vP. It holds:
xe2x88x920.6*nxxe2x89xa6cnx(arg)xe2x89xa6bef(vP,NEx(arg),t) less than onx(arg)xe2x89xa6rnx. 
NE1. Every 55 days are executed (with priority 3) the following operations:
onx(arg).=max(onx(arg)xe2x88x92dn1, 0.6); cnx(arg):=min(cnx(arg)+0.6*dn1, 0); 
bef(vP,NEx(arg),t).=min(bef(vP,NEx(arg),t)+dnb, onx(arg)); 
des(vP,NEx(arg),t).=max(des(vP,NEx(arg),t)xe2x88x922.2*dnb, 0.2); 
where 0.01xe2x89xa6dn1xe2x89xa61 and 0.05xe2x89xa6dnbxe2x89xa64 (sugg.dn1=0.2, dnb=0.25).
The following rules NEw1, . . . ,NEz2.3 have priority 2. Assume, vP has noticed (at time t) new object or situation, OS, in his/her/its surrounding U(t).
NEw1 When vP has identified OS as model OSM (i e the result of algorithms for identification is xe2x80x98OSM is good model of OSxe2x80x99) and has not identified any new property of OS then do not change bef(vP,NEw(OSM),t) and des(vP,NEw(OSM),t).
NEwk1.1. When vP has identified OS as model OSM and the result of identification algorithms is xe2x80x98OS has new property Enxe2x80x99 then.
dnw:=onw(OSM)xe2x88x92cnw(OSM); Nw1=gn1*sqrt(dnw)*1n(1.1+0.2*(bef(vP,NEw(OSM),t)xe2x88x92cnw(OSM))); 
bef(vP,NEw(OSM),t)=max(bef(vP,NEw(OSM),t)xe2x88x92Nw1, cnw(OSM)); 
des(vP,NEw(OSM),t)=min(des(vP,NEw(OSM),t)+1.8*Nw1, 2*dnw); 
where 0.01xe2x89xa6gn1xe2x89xa61 (sugg.: gn1=0.34) 
NEwk1.2. When the property En (mentioned in NEwk1.1) has been entered in the model OSM by appropriate perceiving algorithm (time t1) then.
dnk.=onk(OSM)xe2x88x92cnk(OSM); Nw1.=gn2*sqrt(dnk)*In(1.1+0.2*(bef(vP,NEk(OSM),t1)xe2x88x92cnk(OSM))); 
bef(vP,NEw(OSM),t1)=max(bef(vP,NEw(OSM),t)xe2x88x92Nw1, cnw(OSM)); 
des(vP,NEk(OSM),t1):=min(des(vP,NEw(OSM),t1)+1.8*Nk1, 2*dnw); 
where 0.01xe2x89xa6gn2xe2x89xa61 (sugg. gn2=0.34).
NEwk1.3. If, after the new property En (mentioned in NEwk1.1) has been entered into OSM, the result of applied consistence algorithms (time t2) is xe2x80x98no essential inconsistency of the model OSM is foundxe2x80x99 then:
onw(OSM):=min(onw(OSM)+an1, rnw); cnw(OSM).=max(cnw(OSM)xe2x88x920.6*an1, xe2x88x920.6*rnw); 
dnw.=onw(OSM)xe2x88x92cnw(OSM); Nw2.=gnw*sqrt(dnw)*ln(1.1+0.2*(onw(OSM)xe2x88x92bef(vP,NEw(OSM),t2))); 
bef(vP,NEw(OSM),t2):=min(bef(vP,NEw(OSM),t2)+Nw2, onw(OSM)); 
des(vP,NEw(OSM),t2).=max(des(vP,NFw(OSM),t2)xe2x88x921.6*Nw2, 1); 
onk(OSM):=min(onk(OSM)+an1, rnk); cnk(OSM):=max(cnk(OSM)xe2x88x920.6*an1, xe2x88x920.6*rnk); 
dnk.=onk(OSM)xe2x88x92cnk(OSM); Nk2.=gnw*sqrt(dnk)*1n(1.1+0.2*(onk(OSM)xe2x88x92bef(vP,NEk(OSM),t2))); 
bef(vP,NEk(OSM),t2).=min(bef(vP, NEk(OSM),t2)+Nk2, onk(OSM)); 
des(vP,NEk(OSM),t2):=max(des(vP,NEk(OSM),t2)xe2x88x921.6*Nk2, 1); 
where 0.01xe2x89xa6gntwxe2x89xa61, 0.05xe2x89xa6an1xe2x89xa61 (sugg.: gnw=0.33, an1=0.25).
NEwk1.4. If, after the new property En (mentioned in NEwk1.1) has been entered into OSM, the result of applied consistence algorithms (time t2) is xe2x80x98the model OSM is not consistence with/because.xe2x80x99, then
onw(OSM)=max(onw(OSM)xe2x88x920.7*an1, 0.6); cnw(OSM).=min(cnw(OSM)+0.5*an1, 0); 
dnw:=onw(OSM)xe2x88x92cnw(OSM); Nw2:=gnw*sqr1(dnw)*1n(1.1+0.2*(onw(OSM)xe2x88x92bef(vP,NEw(OSM),t2))); 
bef(vP,NEw(OSM),t2).=min(max(bef(vP,NEw(OSM),t2)+0.2*Nw2, cnw(OSM)), onw(OSM)); 
des(vP,NEw(OSM),t2).=min(max(des(vP,NEw(OSM),t2)xe2x88x920.8*Nw2, 0.2), 1.6*dnw); 
onk(OSM).=max(onk(OSM)xe2x88x920.7*an1, 0.5); cnk(OSM).=min(cnk(OSM)+0.5*an1, 0); 
dnk.=onk(OSM)xe2x88x92cnk(OSM); Nk2=gnw*sqrt(dnk)*1n(1.1+0.2*(onk(OSM)xe2x88x92bef(vP,NEk(OSM),t2))); 
bef(vP,NEk(OSM),t2).=min(max(bef(vP,NEk(OSM),t2)+0.2*Nk2,cnk(OSM)), onk(OSM)); 
des(vP,NEk(OSM),t2)=min(max(des(vP,NEk(OSM),t2)xe2x88x920.8*Nk2, 0.2), 1.6*dnk); 
where 0.01xe2x89xa6gnwxe2x89xa61, 0.05xe2x89xa6an1xe2x89xa61 (sugg.: gnw=0.33, an1=0.25)
NEwk2. If identification of OS has resulted in xe2x80x98vP has no good model of OS; the best model of OS is OSMuxe2x80x99 (time t) then:
build model, OSMs, of OS;
onw(OSM):=min(onw(OSMu)+an1, rnw); 
cnw(OSMs)=max(cnw(OSMu)xe2x88x920.6*an1, xe2x88x920.4*rnw); dnw.=onw(OSMs)xe2x88x92cnw(OSMs); 
bef(vP,NEw(OSMs),t):=onw(OSMs)xe2x88x920.3*dnw; des(vP,NEw(OSMs),t)=0.5*dnw; 
onk(OSMs):=min(onk(OSMu)+an1, rnk); cnk(OSMs):=max(cnk(OSMu)xe2x88x920.6*an1, xe2x88x920.4*rnk); 
dnk.=onk(OSMs)xe2x88x92cnk(OSMs); bef(vP,NEk(OSMs),t):=onk(OSMs)xe2x88x920.3*dnk; 
des(vP,NEk(OSMs),t):=0.5*dnk. 
Property En in NEwk1.1, . . . ,NEwk1.4 and OS in NEwk2 may be a sequence, fws, of words, e g room, person rides a horse. If OS in NEwk2 is a word sequence fws then model, representing the meaning of fws, is built, where fws is the name of this model (semantic of such clauses is outlined in Schurmann [AS2], (1999)).
NEwk3.1 If the result of algorithms for consistency and completeness which were applied to model OSM and models related to OSM (time t) is xe2x80x98inconsistency and incompleteness of the model OSM is not foundxe2x80x99 then.
onw(OSM)=min(onw(OSM)+an1, rnw); cnw(OSM):=max(cnw(OSM)xe2x88x920.6*an1, xe2x88x920.6*rnw); 
bef(vP,NEw(OSM),t):=min(bef(vP,NEw(OSM),t)+1.6*Nw2, onw(OSM)); 
des(vP,NEw(OSM),t):=max(des(vP,NEw(OSM),t)xe2x88x922.6*Nw2, 1); 
onk(OSM):=min(onk(OSM)+an1, rnk); cnk(OSM)=max(cnk(OSM)xe2x88x920.6*an1, xe2x88x920.6*rnk); 
bef(vP,NEk(OSM),t)=min(bef(vP,NEk(OSM),t)+1.6*Nk2, onk(OSM)); 
des(vP,NEk(OSM),t) max(des(vP,NEk(OSM),t)xe2x88x922.6*Nk2, 1); 
where Nw2 and Nk2 are given in NEwk1.3.
NEwk3.2. If the result of algorithms for consistency and completeness which were applied to model OSM and models related to OSM (at time t) is: xe2x80x98model OSM is not consistence with/because . . . xe2x80x99 or xe2x80x98model OSM is not completexe2x80x99, then:
onw(OSM).=max(onw(OSM)xe2x88x921.4*an1, 0.6); cnw(OSM):=min(cnw(OSM)+an1, 0); 
dnw, Nv2 and bef(vP,NEw(OSM),t2)xe2x88x92as in NEwk1 4; 
des(vP,NEw(OSM),t2):=min(max(des(vP,NEw(OSM),t2)xe2x88x921.4*Nw2, 0.2), 1.6*dnw); 
onk(OSM):=max(onk(OSM)xe2x88x921.4*an1, 0.5); cnk(OSM):=min(cnk(OSM)+an1, 0); 
dnk, Nk2xe2x80x94as in NEwk1.4, 
bef(vP,NEk(OSM),t2):=min(max(bef(vP,NEk(OSM),t2)+0.1*Nk2, cnk(OSM)), onk(OSM)); 
des(vP,NEk(OSM),t2):=min(max(des(vP,NEk(OSM),t2)xe2x88x921.5*Nk2, 0.2), 1.5*dnk); 
NEw2.1. When (i) vP executed cognition activity AVE1 (in time (t1,t2)) in order to know whether model OSM have property Em, (ii) the execution of AVE1 decreased bef(vP,be,t1) by ds(be) or prevented the increase of bef(vP,be,t1) by ds(be), for e=1, . . . u, (iii) vP did not find out whether OSM has or has not the property Em, then:
(4.1.1) KA(Em,AVEi)=des(vP,b1,t2)*ds(b1)+. . . +des(vP,bu,t2)*ds(bu);
(4.1.2) if gKA (Em) is not entered in OSM then gKA(Em):=KA(Em,AVEt) else gKA(Em):=gKA(Em)+KA (Em,AVEt); enter gKA(Em) into OSM;
Nw3:=gnw1*1n(1.1+0.2*(bef(vP,NEw(OSM),t2)xe2x88x92cnw(OSM)))*(0.2+sqrt(gKA(Em)));
bef(vP,NEw(OSM),t2):=max(bef(vP,NEw(OSM),t2)xe2x88x92Nw3, cnw(OSM));
des(vP,NEw(OSM),t2).=min(des(vP,NEw(OSM),t2)+1.4*Nw3, 2*(onw(OSM)xe2x88x92cnw(OSM));
where 0.04xe2x89xa6gin1xe2x89xa60.7 (sugg.gnw1=0.15).
KA(Em,AVEi) can be equal to 0. Example of a cognition activity. vP get information about OSM from a person, from vPa or a book
NEw2.2. If conditions (i) and (ii) in NEw2.1 hold and the result of the activity AVEi is either xe2x80x98OSM has the property Emxe2x80x99 or xe2x80x98OSM has not the property Emxe2x80x99 then,
execute operations (4.1.1) and (4.1.2), Nwo:=gwo*1n(1+rnwxe2x88x92onw(OSM))*(0.2+sqrt(gKA(Em))); 
onw(OSM).=min(onw(OSM)+Nwo, rnw); cnw(OSM).=max(cnw(OSM)xe2x88x920.7*Nwo, xe2x88x920.6*rnw); 
Nw4.=gnw2*1n(1.1+0.2*(onw(OSM)xe2x88x92bef(vP,NEw(OSM),t2)))*(0.2+sqrt(gKA(Em))); 
bef(vP,NEw(OSM),t2).=min(bef(vP,NEw(OSM),t2)+Nw4, onw(OSM)); 
des(vP,NEw(OSM),t2).=max(des(vP,NEw(OSM),t2)xe2x88x921.7*Nw4, 1); 
where 0.005xe2x89xa6gwoxe2x89xa60.1, 0.02xe2x89xa6gnw2xe2x89xa60.8 (sugg: gwo=0.035, gnw2=0.19)
NEw2.3. When vP concludes (time t3xe2x89xa7t2) that she/he/it cannot execute more cognition activities AVEt in order to know whether OSM should have the property Em then:
Nwo1.=gwo1*1n(1+onw(OSM)xe2x88x92cnw(OSM))*(0.2+sqrt(gKA(Em))); 
onw(OSM):=max(onw(OSM)xe2x88x92Nwo1, 0.6); cnw(OSM):=min(cnw(OSM)+0.6*Nwo1, 0); 
bef(vP,NEw(OSM),t3):=min(max(bef(vP,NEw(OSM),t3), cnw(OSM)), onw(OSM)); 
des(vP,NEw(OSM),t3):=min(des(vP,NEw(OSM),t3)xe2x88x92gnw3*1n(1.1+des(vP,NEw(OSM),t3))*(0.2+sqrt(gKA(Em))),1) 
where 0.005xe2x89xa6gwo1xe2x89xa60.1, 0.02xe2x89xa6gnw3xe2x89xa60.9 (sugg:gwo1=0.035, gnw3=0.24).
NEwk4. When (i) cognition algorithms of vP found out that object models M(O1), . . . ,M(On) have properties E1, . . . ,Eu (u greater than 1, n greater than 3), (ii) vP has built generalized object model M(Og) having the properties E1, . . . ,Eu, then
onw(M(Og))=max(onw(M(Oi)), for 0 less than ixe2x89xa6n); cnw(M(Og)):=xe2x88x920.6*onw(M(Og)); 
dnw.=onw(M(Og))xe2x88x92cnw(M(Og)); 
bef(vP,NEw(M(Og)),t).=onw(M(Og))xe2x88x920.15*dnw; des(vP,NEw(M(Og)),t):=0.3*dnw); 
onk(M(Og)).=max(onk(M(Oi)), for 0 less than ixe2x88x92n); cnk(M(Og)):=xe2x88x9230.6*onk(M(Og)); 
dnk.=onk(M(Og))xe2x88x92cnk(M(Og)); 
bef(vP,NEk(M(Og)),t).=onk(M(Og))xe2x88x920.2*dnk; des(vP,NEk(M(Og)),t).=0.36*dnk). 
bef(vP,NEz(SM),t). Let ES(t) be the set of situations (i e situation models) which vP can achieve in the present time, and SMz situation which does not belong to ES(t) and which vP wants to achieve.
NEz1. If onz(SMz), bef(vP,NEz(SMz),t) are not defined then:
onz(SMz):=min(onw(SMz), 0.6*rnz); cnz(SMz).=xe2x88x920.6*onz(SMz); Bd(vP).=Bd(vP)∪{NEz(SMz)}; 
bef(vP,NEz(SMz),t):=onz(SMz)xe2x88x920.2*(onz(SMz)xe2x88x92cnz(SMz)); des(vP,NEz(SMz),t)=0.36*(onz(SMz)xe2x88x92cnz(SMz)). 
NEz2.1. When (i) vP executed activity (e g cognition algorithms) AKA (in time (t1,t2)) in order to build new activity AVSz such that the execution of AVSz leads from at least one situation in ES(t1) to the situation SMz, (ii) the execution of AKA decreased bef(vP,be,t1) by ds(be) greater than 0 or prevented the increase of bef(vP,be,t1) by ds(be), for e=1. . . , (iii) vP could not build activity AVSz by activity AKA, then
(4.2 1) AK(AVSz,AKA).=des(vP,b1,t2)*ds(b1)+. . . +des(vP,bu,t2)*ds(bu);
(4.2.2) if gAK(AVSz) is not entered in SMz then gAK(AVSz):=AK(AVSz,AKA)
else gAK(AVSz).=gAK(AVSz)+AK(AVSz,AKA); enter gAK(AVSz) into SMz; 
Nz1:=gnz*1n(1.1+0.2*(bef(vP,NEz(SMz),t2)xe2x88x92cnz(SMz)))*(0.2+sqrt(gAK(AVSz))); 
bef(vP,NEz(SMz),t2).=max(bef(vP,NEz(SMz),t2)xe2x88x92Nz1, cnz(SMz)); 
des(vP,NEz(SMz),t2).=min(des(vP,NFz(SMz),t2)+1.6*Nz1, 2*(onz(SMz)xe2x88x92cnz(SMz)); 
where 0.05xe2x89xa6gnzxe2x89xa60.7 (sugg gnz=0.17).
NEz2.2. When conditions (i), (ii) in NEz2.1 hold and vP has built activity AVSz by AKA then: execute operations (4 2 1) and (4 2 2);
Nzo:=gzo*1n(1+rnzxe2x88x92onz(SMz))*(0.2+sqrt(gAK(AVSz))); 
onz(SMz).=min(onz(SMz)+Nzo, rnz); cnz(SMz).=max(cnz(SMz)xe2x88x920.7*Nzo, xe2x88x920.6*rnz); 
Nz2:=gnz2*1n(1.1+0.2*(onz(SMz)xe2x88x92bef(vP,NEz(SMz),t2)))*(0.2+sqrt(gAK(AVSz))); 
bef(vP,NEz(SMz),t2):=min(bef(vP,NEz(SMz),t2)+Nz2, onz(SMz)); 
des(vP,NEz(SMz),t2):=max(des(vP,NEz(SMz),t2)xe2x88x921.8*Nz2, 1); 
where 0.005xe2x89xa6gzoxe2x89xa60.15, 0.02xe2x89xa6gnz2xe2x89xa60.7 (sugg.: gzo=0.035, gnz2=0.19).
Ez2.3. When vP concludes (time t3xe2x89xa7t2) that he/she/it cannot execute more activities of the kind AKA in order to build activity AVSz then.
Nzo1:=gzo1*1n(1+onz(SMz)xe2x88x92cnz(Smz))*(0.1+sqrt(gAK(AVSz))); 
onz(SMz):=max(onz(SMz)xe2x88x92Nzo1, 0.6); cnz(SMz).=min(cnz(SMz)+0.6*Nzo1, 0); 
bef(vP,NEz(SMz),t3):=max(min(bef(vP,NEz(SMz),t3), onz(SMz)), cnz(SMz)); 
des(vP,NEz(SMz),t3).=max(des(vP,NEz(SMz),t3)xe2x88x92gn3*1n(1.1+des(vP,NEz(SMz),t3))*(0.2+sqrt(gAK(AVSz))),1); 
where 0.005xe2x89xa6gzo1xe2x89xa60.1, 0.02xe2x89xa6gn3xe2x89xa60.9 (sugg.: gzo1=0.035, gn3=0.24).
Situation SMa in ES(t) and SMz may have the following meanings: SMaxe2x88x92xe2x80x98vP has objects O1, . . . , Okxe2x80x99, SMzxe2x88x92xe2x80x98object Ogb is built from objects O1, . . . ,Okxe2x80x99. AVSz is then the activity (the method) which builds the object Ogb from objects O1, . . . ,Ok.
bef(vP,GR,t); GRxe2x80x94to belong to communities. Below, instead of GR we use GR(G)xe2x80x94the need to belong to community G. Each community G has a set NRV(G) of norms, principles, rules and behaviour schemes (models) which ought to be respected and obeyed by members of the community. GR(G) is a secondary need. It emerges in vP when the following conditions are satisfied.
gr1 xcexa3Pxcex5T rosa(vP,P,t) greater than 20* |T|, where T is the set of these members P of the community G for whom vP has model M(P) and |T| denotes the number of elements of set T;
gr2. the result of consistency algorithms applied to NRV(G) is xe2x80x98NRV(G) does not contradict the norms, rules and behaviours in NRV(vP) and NRV(Ga) for communities Ga to which vP belongsxe2x80x99, where NRV(vP) denotes the norms, rules and behaviours of vP;
gr3. vP executed activities AVvk(Pk1, . . . ,Pknk) (k=1, . . . ,r) together with members Pk1, . . . , Pknk of the community G, in time (t1k, t2k) (where t2kxe2x89xa6t), and vP has perceived that AVvk(Pk1, . . . ,Pknk) altered bef(vP,bki,t1k) by
dy(bki,t2k), for i=1, . . . , tk, and r=0 or Zb(G) greater than 2 if r greater than 0, where Zb(G)=0 if r=0, and 
Zb(G)=xcexa3k=1 rxcexa3n=1 tksqrt(desm(bki,t2k)*|dy(bki,t2k|)*sign(dy(bki,t2k)), if r greater than 0 
desm(bkt, t2k)=max(des(vP,bki, t1k), des(vP,bki,t2k)); 
gr4. members Pje (e=1, . . . uj) of the community G executed activities AVu(u=1, . . . ,w), in times (t1u, t2u) (where t2uxe2x89xa6t), and vP has perceived that AVu altered bef(vP,bui,t1u) by dy(bui,t2u), for i=1, . . . ,nu, and w=0 or ZPb(G) greater than 2 if w greater than 0, where ZPb(G)=0 if w=0, and
ZPb(G)=xcexa3n=1 w xcexa3n=nwsqrt(dem(bui,t2u)*|dy(bui,t2u)|)*sign(dy(bui,t2u)), if w greater than 0;
gr5 vP perceivesxe2x80x94believesxe2x80x94(at time t) that if she/he/it belongs to the community G then vP will be able to execute activities AVh1, . . . ,AVhm such that AVhi would increase bef(vP,bhie,t) by sdy(bhie,t), for e=1, . . . , and m=0 or
Zer(G) greater than 3 if m greater than 0, where Zer(G)=0 if m=0, and
Zer(G)=xcexa3i=1 m xcexa3e=1 sqrt(des(vP,bh1e,t)*sdy(bhie,t)), if m greater than 0.
GR1. When conditions gr1, . . . ,gr5 are satisfied and Zb(G)+ZPb(G)+Zer(G) greater than 5 (time t) then
Bd(vP):=Bd(vP)∪{GR(G)}; NRV(vP):=NRV(vP)∪NRV(G); 
ogr(G)=min(max(gc1*(Zb(G)+ZPb(G)+Zer(G)), 1), 16); cgr(G):=xe2x88x920.6*ogr(G); 
bef(vP,GR(G),t):=ogr(G)xe2x88x920.1*(ogr(G)xe2x88x92cgr(G)); des(vP, GR(G),t).=1; 
where 0.05xe2x89xa6gc1xe2x89xa60.4 (sugg. gc1=0.1).
GR2. If GR(G)xcex5Bd(vP), vP executed activity AVv(P1, . . . ,Pn) together with members P1, . . . ,Pn of the community G, in time (t1, t2), and vP has perceived that AVv(P1, . . . ,Pn) altered bef(vP,bi,t1) by dy(bi,t2), for i=1, . . . ,u, then:
Zub=xcexa3t=sqrt(max(des(vP,bi,t1), des(vP,bi,t2))*|dy(bi,t2)|)*sign(dy(b1,t2)); 
ogr(G):=min(ogr(G)+gc2*Zub, 28); cgr(G)=max(cgr(G)xe2x88x920.6*gc2*Zub, xe2x88x9225); 
bef(vP,GR(G),t2).=min(max(bef(vP,GR(G),t2)+gb1*Zub, cgr(G)), ogr(G)); 
des(vP,GR(G),t2):=max(min(des(vP,GR(G),t2)xe2x88x921.7*gb1*Zub, 2*(ogr(G)xe2x88x92cgr(G)), 0.5); 
where 0.03xe2x89xa6gc2xe2x89xa60.4, 0.03xe2x89xa6gb1xe2x89xa60.8 (sugg.gc2=0.1, gb1=0.25).
GR3. Time dependent alteration of bef(vP,GR(G),t). Every 30 days execute following operations, with priority 3:
ogr(G)=max(ogr(G)xe2x88x92gro*1n(1.1+ogr(G)), 0.5); cgr(G)=min(cgr(G)+gro*1n(1.1+|cgr(G|), 0); 
if bef(vP,GR(G),t)xe2x89xa70 then bef(vP,GR(G),t):=max(bef(vP,GR(G),t)xe2x88x92grz*1nn(1.1+bef(vP,GR(G),t) 
xe2x88x92egr(G)), 0) else bef(vP,GR(G),t):=min(max(bef(vP,GR
(G),t), cgr(G)), ogr(G)); 
des(vP,GR(G),t)=min(des(vP,GR(G),t)+grd*1n(20+des(vP,GR(G),t)), 1.8*(ogr(G)xe2x88x92cgr(G)); 
where 0.002xe2x89xa6groxe2x89xa60.2, 0.002xe2x89xa6grzxe2x89xa60.2, 0.002xe2x89xa6grdxe2x89xa60.2 (sugg.gro=0.027, grz=0.03, grd=0.031)
Rules GR1, GR2, GR4, GR5 have priority 2. bef(vP,GR(G),t) decreases when vP is in situation Sg1 and Sg2:
Sg1: The result of perceiving and cognition algorithms of vP (at time t) is: if I executed activity AVw together with members of the community G then bef(vP,bwi,t) would alter by day(bwi), for i=1, . . . ,u greater than 0, so that:
Zua(t)=xcexa3t=1 w sqrt(des(vP,bwi,t)*|day(bwi,t|)*sign(day(bwi)) greater than 2. 
Sg2: Because members of the community G cannot execute or do not want to execute activity AVw together with vP (e g vP is separated from the community G), vP executes activity AVr (instead AVw), in time (t,t1), which alters bef(vP,bwi,t) by dy(bwi,t1), for i=1, . . . u, so that:
dZug(t1)=Zua(t)xe2x88x92xcexa3t=1 sqrt(max(des(vP,bwi,t), des(vP,bwi,t1))*|dy(bwi,t1|)* sign(dy(bwi,t1)) greater than 2. 
GR4. When vP is in situation Sg1 and Sg2 then:
ogr(G):=min(ogr(G)+gr1*dZug(t1), 26); cgr(G).=max(cgr(G)xe2x88x920.7*gr1*dZug(t1), xe2x88x9225); 
bef(vP,GR(G),t1).=max(bef(vP,GR(G),t1)xe2x88x922.5*gr1*dZug(t1), cgr(G)); 
des(vP,GR(G),t1):=min(des(vP,GR(G),t)+4.5*gr1*dZug(t1), 1.8*(ogr(G)xe2x88x92cgr(G)); 
where 0.03xe2x89xa6gr1xe2x89xa60.4 (sugg. gr1=0.1).
GR5. When members of community G executed activity AVgv, in time (t, t1), and vP has perceived that AVgv hanged bef(vP,bgi,t) by dy(bgi), for i=1, . . . ,u, so that
Zug(t1)=xcexa3n=1 wsqrt(max(des(vP,bgi, t), des(vP,bgi,t1))*|dy(bgi)|)*sign(dy(bgi)) less than xe2x88x922 
then:
ogr(G):=max(ogr(G)+gr3*Zug(t1), 0.5); cgr(G):=min(cgr(G)xe2x88x920.7*gr3*Zug(t1), 0); 
bef(vP,GR(G),t1).=min(max(bef(vP,GR(G),t1)+3*gr3*Zug(t), cgr(G)), ogr(G)); 
des(vP,GR(G),t1).=max(des(vP,GR(G),t1)+gr3*Zug(t1), 0); 
where 0.03xe2x89xa6gr3xe2x89xa60.4 (sugg. gr3=0.1).
bef(vP,MA,t); MAxe2x80x94to have power over people and animals. Let OP and OPj (j=1,2, . . . ) denote an artificial creature (e g vP), a virtual organization or institution, virtual human or animal, virtual group of people, virtual deity Human has an innate need for power over people and animals. We assume,
xe2x88x9228xe2x89xa6cma(OP)xe2x89xa6bef(OP,MA,t)xe2x89xa6oma(OP)xe2x89xa630 and bef(OP,MA,ts)=0.4*oma(OP) is the initial value, where oma(OP) greater than 0, oma(OP) and cma(OP) are determined individually for each OP. Below, instead of cma(OP) and oma(OP), we write cma and oma.
bef(vP,MA,t) and des(vP,MA,t) change in following cases.
m1. vP ordered OP to execute or to stop activity AVop, or vP executed activity AVv to cause OP to execute or to stop activity AVop (OP stops activity AVop if OP breaks off this activity or OP will not do this activity in future);
m2. vP has made OP harm and OP has to bear it (vP harms OP when vP decreases bef(OP,b,.), for some needs bxcex5Bd(OP));
m3 vP gets more power or vP loses power;
m4. time dependent decrease of desire for power.
In order to formulate situations m1, m2, m3 more precisely, we introduce new stimulus pattern and situations Sub, Sun, SVub, SVun which describe the power relations between vP and OP. The new stimulus pattern has the form:
((Nba, Nb), eru(OP,b)=x; where C) 
where xe2x88x9250xe2x89xa6xxe2x89xa650 and C denotes a condition. If condition C holds and this pattern is applied then bef(OP,b,t), des(OP,b,t), epr(OP,eru, . . . ) and enr(OP,eru, . . . ) are determined as follows
(4.3) if xxe2x89xa70 then begin
Zu.=gd*x*1n(1+0.1*(og(b)xe2x88x92bef(OP,b,t))); bef(OP,b,t).=min(bef(OP,b,t)+Zu, og(b)); 
des(OP,b,t).=max(des(OP,b,t)xe2x88x921.7*Zu, 1) end 
else been Zu.=gd*x*1n(1+0.1*(bef(OP,b,t)xe2x88x92cg(b))); bef(OP,b,t).=max(bef(OP,b,t)+Zu, cg(b)); 
des(OP,b,t)=min(des(OP,b,t)xe2x88x921.7*Zu, 2*(og(b)xe2x88x92cg(b))) end; 
where
cg(b)xe2x89xa6bef(OP,b,t)xe2x89xa6og(b), 0.6xe2x89xa6gdxe2x89xa61.2 (sugg. gd=0.9). 
epr(OP,eru,b,a,t):=(Nba/Nb)*des(OP,b,t)*dzu*23, if xxe2x89xa70; 
enr(OP,eru,b,a,t):=(Nba/Nb)*des(OP,b,t)*dab*23, if xxe2x89xa60; 
where dzu=bef(OP,b,t)xe2x88x92bfb, dab=bfbxe2x88x92befOP,b,t,), bfb is the value of bef(OP,b,t) before execution of operation
(4.3) and bef(OP,b,t) is the value after execution of operation (4.3).
Sub: Mub(vP,OP,Npa,Np;(Ber;Cu); dpm(vP); nRM(OP))
where: Npaxe2x89xa6Np, Npa and Np are natural numbers, Berxe2x80x94(the scope of power of vP over OP) is a set of orders which vP may give OP, Cuxe2x80x94conditions for execution of orders from Ber, dpm(vP)xe2x80x94some patterns of the form xe2x80x98(Nma, Nm), eru(vP,MA)=x; where Cxe2x80x99, where xxe2x89xa70, nRM(OP)xe2x80x94a set of negative stimulus patterns of the form xe2x80x98(Nobia, Nobi), fsn(OP,bi)=. . . ; where CAixe2x80x99, where fsn denotes enb, unb, enbu, unbu, vpb, eru The meaning: When vP gives an order belonging to Ber to OP and conditions in Cu hold then OP will execute this order with probability Npa/Np. If OP does not execute this order (although conditions in Cu hold) then OP will be punished (with probability Nobia/Nobi) by negative stimulus according to appropriate pattern xe2x80x98 . . . fsn(OP,bi)=.xe2x80x99 in nRM(OP). When vP achieves situation Sub then bef(vP,MA,.) increases according to appropriate pattern in dpm(vP).
Sun: Mun(vP, OP, (Berop;Ct); dnm(vP); Nua,Nu, nRM(vP)),
where. Beropxe2x80x94(the scope of power of OP over vP) a set of orders which OP may give to vP, Cuxe2x80x94conditions for execution of orders in Berop, rosa(vP,Anu,t) less than 0 for Anu in Berop, dnm(vP)xe2x80x94some patterns of the form xe2x80x98(Nma, Nm), eru(vP,MA)=y; where Cxe2x80x99, where 0xe2x89xa7y, nRM(vP)xe2x80x94a set of negative stimulus patterns of the form xe2x80x98(Nobia, Nob1), fsn(vPbi)= . . . ; where CAixe2x80x99. The meaning. When vP has received order Anuxcex5Berop from OP and conditions in Cu hold then vP must execute the order Anu. If vP does not execute order Anu (although conditions in Cu hold) then vP will be punished (with probability Nua/Nu) by negative stimulus according to appropriate pattern xe2x80x98 . . . fsni(vP,bi)= . . . xe2x80x99 in nRM(vP).
SVub: MVub(vP, AVv, OP, Nvoa,Nvo; AVop; where Cu). The meaning. When vP has executed activity AVv and conditions in Cu hold then vP expects that OP executes (or stops) activity AVop with probability Nvoa/Nvo.
SVun. MVun(OP, AVo; vP, AVp; where Cu; Nsa,Ns, SAVs),
where SAVs denotes a situation or an activity, |rosa(vP,SAVs,t)| greater than 2 and rosa(vP,AVp,t) less than xe2x88x922. When OP executes activity AVo, conditions in Cu hold and vP does not execute (or stop) activity AVp then vP will be (with probability Nva/Ns) in one of the following two situations:
(un1) vP is in situation SAVs (or must execute activity SAVs, respectively), if rosa(vP,SAVs,t) less than xe2x88x922;
(un2) vP does not achieve situation SAVs or vP must leave situation SAVs (or vP cannot execute activity SAVs), if rosa(vP, SAVs, t) greater than 2.
If vP executes activity AVp then vP will be neither in situation (un1) nor in situation (un2).
The following rules (they have priority 2, except MA4) describe more exactly changes of bef(vP,MA,t).
MA1.1. When (i) vP is in situation Sub, (ii) vP has given order An1xcex5Ber to OP (time t), where conditions in Cu hold, (iii) OP executed the order An1, then.
Maz1.=gmz1*(d1xe2x88x92Npa/Np)*1n(1.1+0.2*(omaxe2x88x92bef(vP,MA,t)))*sqrt(1+eres(vP,An1,t)); 
bef(vP,MA,t).=min(bef(vP,MA,t)+Maz1, oma); des(vP,MA,t):=max(des(vP,MA,t)xe2x88x921.2*Maz1, 1); 
where
1xe2x89xa6d1xe2x89xa61.4, 0.005xe2x89xa6gmz1xe2x89xa60.3 (sugg.: d1=1.2, gmz1=0.08) and eres(vP,An1,t)=rosa(vP,An1,t) if rosa(vP,An1,t) greater than 0 (and is defined), otherwise eres(vP,An1,t)=0. 
MA1.1.1 If conditions (i) and (ii) in MA1.1 hold and OP refused to execute order An1 then.
Mab1.=gma1*(a1+Npa/Np)*1n(1.1+0.2*(bef(vP,MA,t)xe2x88x92cma))*sqrt(1+eres(vP,An1,t)); 
bef(vP,MA,t).=max(bef(vP,MA,t)xe2x88x92Mab1, cma); des(vP,MA,t):=min(des(vP,MA,t)+1.8*Mab1, 1); 
where 0xe2x89xa6a1xe2x89xa60.4, 0.008xe2x89xa6gma1xe2x89xa60.5 (sugg. a1=0.05, gma1=0.12).
MA1.2. When (i) vP is in Situation Sun, (ii) vP has received order Anuxcex5Berop from OP, where conditions in Cu hold, (iii) vP has executed order Anu (time t), then:
M12.=gm12*(d1xe2x88x92Nua/Nu)*1n(1+0.2*(bef(vP,MA,t)xe2x88x92cma))*sqrt(1+|rosa(vP,Anu,t)|); 
bef(vP,MA,t).=max(bef(vP,MA,t)xe2x88x92M12, cma); des(vP,MA,t):=min(des(vP,MA,t)+1.6*M12, 2*(omaxe2x88x92cma))); 
where 0.005xe2x89xa6gm12xe2x89xa60.4 (sugg.: gm12=0.08) and d1 is given in MA1.1
MA1.2.1 When conditions (i) and (ii) in MA1.2 hold, vP has refused to execute the order Anu and vP has not been punished by negative stimuli given in nRM(vP,Anu), then
Ma3:=gm11*(a1+Nua/Nu)*1n(1.1+0.2*(omaxe2x88x92bef(vP,MA,t)))*sqrt(1+|rosa(vP,nRM(vP,Anu),t)|); 
bef(vP,MA,t):=min(bef(vP,MA,t)+Ma3, oma); des(vP,MA,t).=max(des(vP,MA,t)xe2x88x921.3*Ma3, 1); 
where 0.006xe2x89xa6gm11xe2x89xa60.6 (sugg gm11=0.09), a1 is given in MA1.1.1 and nRM(vP,Anu) are the patterns in nRM(vP) which are applied when vP refuses to execute the order Anu.
MA1.2.2 When conditions (i) and (ii) in MA1.2 hold, vP has refused to execute the order Anu and vP has been punished by negative stimuli according to patterns in nRM(vP,Anu), then:
M22=gm2*(d1xe2x88x92Nua/Nu)*1n(1.1+0.2*(bef(vP,MA,t)xe2x88x92cma))*sqrt(1+|rosa(vP,nRM(vP,Anu),t)|); 
bef(vP,MA,t):=max(bef(vP,MA,t)xe2x88x92M22, cma); des(vP,MA,t)=min(des(vP,MA,t)+2*M22, 2*(omaxe2x88x92cma))); 
where 0.008xe2x89xa6gm2xe2x89xa60.7 (sugg. gm2=0.165) and d1 is given in MA1.1.
MA1.3. When (i) vP is in situation SVub, (ii) vP has executed activity AVv with respect to OP, where conditions in Cu hold, (iii) OP executed (or stopped execution of) activity AVop (time t) as vP has wished, then.
Maz2.=gmz2*(d1xe2x88x92Nvoa/Nvo)*1n(1.1+0.2*(omaxe2x88x92bef(vP,MA,t)))*sqrt(1+eres(vP,AVop,t)); 
bef(vP,MA,t).=min(bef(vP,MA,t)+Maz2, oma); des(vP,MA,t):=max(des(vP,MA,t)xe2x88x921.2*Maz2, 1); 
where 0.006xe2x89xa6gmz2xe2x89xa60.6 (sugg. gmz2=0.09) and d1, eres are given in MA1.1.
MA1.3.1. When conditions (i) and (ii) in MA1.3 hold and OP refused to execute (or to stop) activity AVop (time t) then:
Mab2=gma2*(a1+Nvoa/Nvo)*1n(1.1+0.2*(bef(vP,MA,t)xe2x88x92cma))*sqrt(1+eres(vP,AVop,t)); 
bef(vP,MA,t):=max(bef(vP,MA,t)xe2x88x92Mab2, cma); des(vP,MA,t):=min(des(vP,MA,t)+1.8*Mab2, 2*(omaxe2x88x92cma)); 
where 0.006xe2x89xa6gma2xe2x89xa60.6 (sugg. gmz2=0.13) and a1 is given in MA1.1.1.
MA1.4. When (i) vP is in situation SVun, (ii) OP has executed activity AVo with respect to vP, where conditions in Cu hold, (iii) vP has executed (or stopped the execution of) activity AVp (time t) as OP wished, then:
Mav1.=gmv*(d1xe2x88x92Nsa/Ns)*1n(1.1+0.2*(bef(vP,MA,t)xe2x88x92cma))*sqrt(1+|rosa(vP,AVp,t)|); 
bef(vP,MA,t):=max(bef(vP,MA,t)xe2x88x92Mav1, cma); des(vP,MA,t):=min(des(vP,MA,t)+1.7*Mav1, 2*(omaxe2x88x92cma)); 
where 0.006xe2x89xa6gmvxe2x89xa60.6 (sugg. gmv=0.09) and d1 is given in MA1.1.
MA1.4.1. When conditions (i) and (ii) in MA1.4 hold, vP has not executed (does not stop execution of) activity AVp and neither (un1) nor (un2) (in SVun) has taken place (Zeitp t), then:
Mavz:=gmov1*(a1+Nsa/Ns)*1n(1.1+0.2*(omaxe2x88x92bef(vP,MA,t)))*sqrt(1+|rosa(vP,AVp,t)|); 
bef(vP,MA,t).=min(bef(vP,MA,t)+Mavz, oma); des(vP,MA,t):=max(des(vP,MA,t)xe2x88x921.3*Mavz, 1); 
where 0.006xe2x89xa6gmov1xe2x89xa60.6 (sugg gmov1=0.07) and a1 is given in MA1.1.1.
MA1.4.2. When conditions (i) and (ii) in MA1.4 hold, vP has not executed (does not stop execution of) activity AVp and either (un1) or (un2) has taken place (Zeitp t), then:
Mav2.=gmv2*(d1xe2x88x92Nsa/Ns)*1n(1.1+0.2*(bef(vP,MA,t)xe2x88x92cma))*sqrt(1+|rosa(vP,SAVs,t)|); 
bef(vP,MA,t):=max(bef(vP,MA,t)xe2x88x92Mav2, cma); des(vP,MA,t):=min(des(vP,MA,t)+1.8*Mav2, 2*(omaxe2x88x92cma)); 
where 0.007xe2x89xa6gmv2xe2x89xa60.7 (sugg. gmv2=0.17) and d1 is given in MA1.1.
MA2.1 When (i) OP has executed activity AVsop (time t) which has harmed (or may harm) vP (rosa(vP,AVsop,t) less than xe2x88x923), (ii) vP perceives that OP has executed activity AVsop in order to harm vP, (iii) vP can/could prevent/diminish the harm of the activity AVsop only in degree 0xe2x89xa6pvxe2x89xa61, then
Mab3=gm3*(1.1xe2x88x92pv)*1n(1.1+0.2*(bef(vP,MA,t)xe2x88x92cma))*sqrt(1+|rosa(vP,AVsop,t)|); 
bef(vP,MA,t):=max(bef(vP,MA,t)xe2x88x92Mab3, cma); des(vP,MA,t):=min(des(vP,MA,t)+2*Mab3, 2*(omaxe2x88x92cma)); 
where 0.008xe2x89xa6gm3xe2x89xa60.7 (sugg. gm3=0.17).
MA2.1.1. When conditions (i) and (iii) in MA2.1 hold and, according to vP, OP has not executed activity AVsop in order to harm (has not had the intention to harm) vP then.
bef(vP,MA,t):=max(bef(vP,MA,t)xe2x88x920.2*Mab3,cma); des(vP,MA,t):=min(des(vP,MA,t)+0.35*Mab3, 2*(omaxe2x88x92cma)) 
where Mab3 is given in MA2.1.
MA2.2. When (i) vP has executed activity AVsv (time t) in order to harm OP (according to vP, rosa(OP,AVsv,t) less than 0), (ii) OP can/could prevent/diminish the harm of the activity AVsv only in degree 0xe2x89xa6psxe2x89xa61, then.
Maz3:=gmz3*(0.9xe2x88x92ps)*1n(1.1+0.2*(omaxe2x88x92bef(vP,MA,t)))*sqrt(4+|wpros(OP,AVsv,t)|); 
bef(vP,MA,t):=max(min(bef(vP,MA,t)+Maz3, oma), cma); des(vP,MA,t):=max(des(vP,MA,t)xe2x88x921.4*Maz3, 1); 
where 0.008xe2x89xa6gmz3xe2x89xa60.6 (sugg gmz3=0.12) and vP perceives value rosa(OP,AVsv,t) as wpros(OP,AVsv,t).
MA3.1 When vP has achieved new situation Sub (time 1) then determine bef(vP,MA,t) and des(vP,MA,t) by the valid pattern xe2x80x98(Nma, Nm), eru(vP,MA)=x; . . . xe2x80x99 in dpm(vP). After this pattern has been applied (s. (4.3)), replace this pattern, in dpm(vP), by xe2x80x98((10,10), eru(vP,MA)=xe2x88x92x; where vP leaves Sub)xe2x80x99.
MA3.1.1 When vP leaves situation Sub (ceases to be in situation Sub, time t) and in dpm(vP) is pattern xe2x80x98((10,10), eru(vP,MA)=x1; where vP leaves Sub)xe2x80x99 (x1 less than 0) then decrease bef(vP,MA,t) and increase des(vP,MA,t) by the pattern xe2x80x98eru(vP,MA)=x1xe2x80x99 as given in (4.3) (where b=MA).
MA3.2. When vP has got in situation Sun (time t) then determine bef(vP,MA,t) and des(vP,MA,t) by the valid pattern xe2x80x98(Nma, Nm), eru(vP,MA)=y; . . . xe2x80x99 in dnm(vP). After this pattern has been applied (s. (4.3)), replace this pattern (in dnm(vP)) by xe2x80x98((10,10), eru(vP,MA)=xe2x88x92y; where vP leaves Sun)xe2x80x99.
MA3.2.1. When vP leaves situation Sun (time t) and pattern xe2x80x98((10,10), eru(vP,MA)=y1; where vP leaves Sun)xe2x80x99 (y1 greater than 0) is in dnm(vP) then increase bef(vP,MA,t) and decrease des(vP,MA,t) by the pattern xe2x80x98eru(vP,MA)=y1xe2x80x99 as given in (4.3) (where b=MA).
MA3.3 When vP has achieved situation SVub (time t) then:
Maz5.=gmz2*(a1+Nvoa/Nvo)*1n(1.1+0.2*(omaxe2x88x92bef(vP,MA,t)))*sqrt(1+eres(vP,AVop,t)); 
bef(vP,MA,t).=min(bef(vP,MA,t)+1.4*Maz5, oma); des(vP,MA,t).=max(des(vP,MA,t)xe2x88x921.5*Maz5, 1); 
where gmz2 and eres (here and in MA3 3 1) have the same meaning as in MA1.3.
MA3.3.1. When vP leaves situation SVub (time t) then:
Maz6.=gmz2*(a1+Nvoa/Nvo)*1n(1.1+0.2*(bef(vP,MA,t)xe2x88x92cma))*sqrt(1+eres(vP,AVop,t)); 
bef(vP,MA,t):=max(bef(vP,MA,t)xe2x88x921.4*Maz6, cma); des(vP,MA,t).=min(des(vP,MA,t)+1.5*Maz6, 2*(omaxe2x88x92cma)) 
MA3.4 When vP has got in situation SVun (time t) then.
Mav3.=gmv*(a1+Nsa/Ns)*1n(1.1+0.2*(bef(vP,MA,t)xe2x88x92cma))*sqrt(1+|rosa(vP,AVp,t)|); 
bef(vP,MA,t).=max(bef(vP,MA,t)xe2x88x921.4*Mav3, cma); des(vP,MA,t):=min(des(vP,MA,t)+1.6*Mav3,2*(omaxe2x88x92cma)), 
where, here and in MA3.4.1, gmv is given in MA1.4.
MA3.4.1 When vP leaves situation SVun (time t) then
Mav4=gmv*(a1+Nsa/Ns)*1n(1.1+0.2*(omaxe2x88x92bef(vP,MA,t)))*sqrt(1+|rosa(vP,AVp,t)|); 
bef(vP,MA,t):=min(bef(vP,MA,t)+1.4*Mav4, oma); des(vP,MA,t)=max(des(vP,MA,t)xe2x88x921.5*Mav4, 1). 
MA4. Time dependent alterationxe2x80x94every d hours execute (with priority 3) the following operations:
bef(vP,MA,t).=min(bef(vP,MA,t)+gmt*1n(1.1+0.05*(1xe2x88x92bef(vP,MA,t)), 0), if bef(vP,MA,t) less than 0; 
des(vP,MA,t).=max(des(vP,MA,t)xe2x88x92gmt1*1n(1.1+0.2*des(vP,MA,t)), 1); 
where gmt and gmt1 depend on vP, 0.004xe2x89xa6gmtxe2x89xa60.2, 0.005xe2x89xa6gmt1 less than 0.2 (e g gmt=0.014, gmt1=0.02, d=3).
bef(vP,LI,t); LIxe2x80x94need for liking and love. It is a collective notion. LI consists of needs LI(vP,OSA)xe2x80x94liking and love of vP to OSAxe2x80x94where OSA denotes an object, a situation or an activity. LI(vP,OSA) is close connected with the expectation of vP that OSA increases bef(vP,b,t) or prevents the decrease of bef(vP,b,t), for some needs b.